Free-breathing liver fat and quantification using motion-corrected averaging based on a nonlocal means algorithm

To propose a motion-robust chemical shift-encoded (CSE) method with high signal-to-noise (SNR) for accurate quantification of liver proton density fat fraction (PDFF) and $$ .

A free-breathing multi-repetition 2D CSE acquisition with motion-corrected averaging using nonlocal means (NLM) was proposed. PDFF and $$ quantified with 2D CSE-NLM were compared to two alternative 2D techniques: direct averaging and single acquisition (2D 1ave) in a digital phantom. Further, 2D NLM was compared in patients to 3D techniques (standard breath-hold, free-breathing and navigated), and the alternative 2D techniques. A reader study and quantitative analysis (Bland-Altman, correlation analysis, paired Student's t-test) were performed to evaluate the image quality and assess PDFF and $$ measurements in regions of interest.

In simulations, 2D NLM resulted in lower standard deviations (STDs) of PDFF (2.7%) and $$ (8.2  $$ ) compared to direct averaging (PDFF: 3.1%, $$ : 13.6  $$ ) and 2D 1ave (PDFF: 8.7%, $$ : 33.2  $$ ). In patients, 2D NLM resulted in fewer motion artifacts than 3D free-breathing and 3D navigated, less signal loss than 2D direct averaging, and higher SNR than 2D 1ave. Quantitatively, the STDs of PDFF and $$ of 2D NLM were comparable to those of 2D direct averaging (p>0.05). 2D NLM reduced bias, particularly in $$ (-5.73 to -0.36  $$ ) that arises in direct averaging (-3.96 to 11.22  $$ ) in the presence of motion.

2D CSE-NLM enables accurate mapping of PDFF and $$ in the liver during free-breathing.